Cleaning and Reporting on Education 5W data from Venezuela

A. Cleaning the 5W data

This first section details an example of the code necessary for cleaning a 5W dataset in R. 5W data is typically messy and in wide format. Additionally, data wrangling is time consuming and an inefficient use of a specialist’s time. The code presented below intends to demonstrate that this monthly (or sometimes more frequent) occurence can be greatly automated, allowing for more timely and accurate analysis as well as creating the space for more tailored reporting.

 

1. Reading in the dataset

# specifying column types 
act_col_types <- c("date", "date", "text", "text","text","text",
                   "text","text","text","text","text","text","text",
                   "numeric", "numeric", "text","text","text",
                   "text","text","text", "numeric", "text", 
                   "date", "date", "text", "numeric", "numeric",
                   "logical", "numeric", "numeric", "numeric",
                   "numeric", "numeric", "numeric", "numeric",
                   "numeric", "numeric", "numeric", "numeric",
                   "numeric", "numeric", "numeric")

# you're gonna have to live with the error messages,
# since you can't slice before read_excel() argument

act1<- read_excel("database activities 5W.xlsx", 
                     sheet = "data", 
                     skip = 1, 
                     col_types = act_col_types) %>% 
  clean_names() %>% 
  remove_empty() %>% 
  slice(-c(1)) %>% # removes the second row
  slice(-c(12055)) # removes the grand total

1a. Renaming Columns

A glimpse() of the renamed dataset:

## Rows: 12,054
## Columns: 43
## $ fecha_reportado_cluster           <dttm> NA, NA, NA, NA, NA, NA, NA, NA, NA,~
## $ mes_reportado                     <dttm> 2020-01-31, 2020-01-31, 2020-01-31,~
## $ codigo_de_proyecto                <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, ~
## $ nombre_del_proyecto               <chr> "Todos y Todas a la Escuela", "Todos~
## $ organizacion_lider                <chr> "UNICEF - Fondo de las Naciones Unid~
## $ organizacion_implementadora       <chr> "SOCIO 1", "SOCIO 1", "SOCIO 1", "SO~
## $ estado                            <chr> "Zulia", "Zulia", "Zulia", "Distrito~
## $ pcode1                            <chr> "VE23", "VE23", "VE23", "VE01", "VE0~
## $ municipio                         <chr> "Maracaibo", "Maracaibo", "Maracaibo~
## $ pcode2                            <chr> "VE2313", "VE2313", "VE2313", "VE010~
## $ parroquia                         <chr> "Luis Hurtado Higuera", "Luis Hurtad~
## $ pcode3                            <chr> "VE231312", "VE231312", "VE231312", ~
## $ ubicacion                         <chr> "E. T. Dr. Luis Razetti  Fe Y Alegrí~
## $ latitud                           <dbl> 10.596934, 10.596934, 10.596934, 10.~
## $ longitud                          <dbl> -71.66896, -71.66896, -71.66896, -66~
## $ actividad_full                    <chr> "CLEDU/CA2.09: Formación docente y o~
## $ actividad_codigo                  <chr> "CA2.09", "CA2.09", "CA2.09", "CA2.0~
## $ actividad_desc                    <chr> "Formación docente y otro personal e~
## $ actividad_codigo_desc             <chr> "CA2.09: Formación docente y otro pe~
## $ act_covid                         <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, ~
## $ unidad                            <chr> "#Personas", "#Personas", "#Personas~
## $ cantidad_del_mes                  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, ~
## $ recurrente_beneficiarios          <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, ~
## $ fecha_de_inicio                   <dttm> 2019-10-22, 2019-10-15, 2020-01-13,~
## $ fecha_prevista_finalizacion       <dttm> 2020-01-20, 2020-01-13, 2020-01-13,~
## $ estatus_actividad                 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, ~
## $ total_beneficiarios               <dbl> 16, 13, 41, 2, 1, 25, 19, 19, 17, 17~
## $ check_beneficiaries               <dbl> 16, 13, 41, 2, 1, 25, 19, 19, 17, 17~
## $ t_f_ben                           <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, ~
## $ percent_poblacion_indigena        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, ~
## $ percent_personas_con_discapacidad <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, ~
## $ m_0_3                             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ~
## $ m_3_6                             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ~
## $ m_7_12                            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ~
## $ m_12_17                           <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ~
## $ m_18_19                           <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ~
## $ f_0_3                             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ~
## $ f_3_6                             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ~
## $ f_7_12                            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ~
## $ f_12_17                           <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ~
## $ f_18_19                           <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ~
## $ f_mayores_de_19                   <dbl> 11, 4, 41, 2, 1, 20, 10, 10, 5, 8, 3~
## $ m_mayores_de_19                   <dbl> 5, 9, 0, 0, 0, 5, 9, 9, 12, 9, 13, 1~

 

2. String Manipulation and Recoding

2a. Removing accents and standardising case

Whilst it is true when some people say that if you remove tildes and other diacritical marks, it it no longer becomes Spanish. However, it must be said that implementing partners don’t care and have included a wide variety of spellings. I would say that remove all accent marks is necessary to continue working with the dataset.

# function to remove accents
rm_accent <- function(colns){
  colns <- stri_trans_general(colns, "Latin-ASCII")
}

# removing accents
# and str_to_upper() since R is case-sensitive
act1 <- act1 %>% 
  mutate(estado    = rm_accent(str_to_upper(estado)), 
         municipio = rm_accent(str_to_upper(municipio)),
         parroquia = rm_accent(str_to_upper(parroquia)),
         ubicacion = rm_accent(str_to_upper(ubicacion)),
         actividad_desc = rm_accent(str_to_upper(actividad_desc)))

2b. Recoding erroneous data

These should just be dropdown menus; partners should also be informed that blanks will be treated as FALSE. Additionally, a new date column just with the month is mutated, as the mes_reportado column has some months with multiple reporting dates.

# recoding 
act1 <- act1 %>% 
  mutate(recurrente_beneficiarios = 
           recode(recurrente_beneficiarios, 
            "no" = FALSE, "No" = FALSE, "Si" = TRUE, "si" = TRUE, "sí" = TRUE, "Sí" = TRUE),
         act_covid = recode(act_covid, 
            "no" = FALSE, "No" = FALSE, "Si" = TRUE, "si" = TRUE, "sí" = TRUE, "Sí" = TRUE)) %>% 
  replace_na(list(recurrente_beneficiarios = FALSE, act_covid = FALSE)) 

# recoding estatus_actividad
act1$estatus_actividad <- act1$estatus_actividad %>% 
  str_replace_all(c("En ejecucion" = "ejecucion", 
                    "en ejecución" = "ejecucion", 
                    "en Ejecución" = "ejecucion",
                    "En ejecución" = "ejecucion",
                    "En Ejecución" = "ejecucion",
                    "finalizada" = "finalizada",
                    "Finalizada" = "finalizada"))

# create a new variable of last day of month only 
act1$mes_solo <- ceiling_date(act1$mes_reportado, "month") - days(1)

 

3. Cleaning locations

3a. Read in locations.csv

And check if you need to update it.

locations <- read_csv("locations.csv") %>%
  mutate(estado    = rm_accent(str_to_upper(estado)), # just to make sure 
         municipio = rm_accent(str_to_upper(municipio)),
         parroquia = rm_accent(str_to_upper(parroquia)),
         ubicacion = rm_accent(str_to_upper(ubicacion)))

Maybe we should add new pcodes for “todo municipio” and “todo estado”; will they be abused?

# see if you need to update locations 
# new locations not in locations.csv will be caught by the anti-join()
 locations_add <- act1 %>% 
   select(estado, pcode1, municipio, pcode2, parroquia, pcode3, ubicacion,
          latitud, longitud) %>% 
   distinct() %>% 
   anti_join(locations, by = "ubicacion")

3b. Splitting the dataset into clean and dirty

# splitting into two datasets, one clean one dirty. 
adm_dirty <- act1 %>% 
  filter(is.na(estado) | is.na(pcode1) |
         is.na(municipio) | is.na(pcode2) |
         is.na(parroquia) | is.na(pcode3))

adm_clean <- act1 %>% 
  filter(!is.na(estado) & !is.na(pcode1) &
         !is.na(municipio) & !is.na(pcode2) &
         !is.na(parroquia) & !is.na(pcode3)) 

3c. Fill in missing values with coalesce()

The locations reference dataset is used to clean adm_dirty and rewrite it. The distinct() call at the end is to ensure that no duplicates are included as the left_join() argument will produce duplicates (still not sure how to control this). After which, adm_clean and adm_dirty are combined into act2.

# filling in missing values
adm_dirty <- adm_dirty %>% 
  left_join(locations, by = "ubicacion") %>% 
  mutate(estado    = coalesce(estado.x, estado.y),
         pcode1    = coalesce(pcode1.x, pcode1.y),
         municipio = coalesce(municipio.x, municipio.y),
         pcode2    = coalesce(pcode2.x, pcode2.y),
         parroquia = coalesce(parroquia.x, parroquia.y),
         pcode3    = coalesce(pcode3.x, pcode3.y)) %>% 
  select(-estado.x, -estado.y, -pcode1.x, -pcode1.y,
         -municipio.x, -municipio.y, -pcode2.x, -pcode2.y,
         -parroquia.x, -parroquia.y, -pcode3.x, -pcode3.y) %>% 
  distinct() # removing duplicate rows from the join 

# bind_rows() does not care about column sequence 
# but put adm_clean first so that the original order is preserved
act2 <- bind_rows(adm_clean, adm_dirty)

3d. Error checking the join

printing total beneficiaries for act1 and act2, followed by nrow() for both.

##          [,1]
## [1,] 28965878
## [2,] 28965878
## [3,]    12054
## [4,]    12054

 

4. Beneficiary disaggregation

This is to check that total_beneficiarios is equal to the sum of all disaggregated columns. First, we mutate a new column by summing all the disaggregated beneficiaries and calling it total_ben_check. Then, we mutate a new column in thr dataset called no_esp_ben or beneficiarios no especificados so that we maintain the un-disaggregated beneficiaries without polluting the rest of the dataset.

# mutating bencheck and unspecified beneficiaries columns 
act2 <- act2 %>% 
  mutate(total_ben_check = select(., m_0_3:m_mayores_de_19) %>%  rowSums(na.rm = TRUE), 
         no_esp_ben      = round(total_beneficiarios) - round(total_ben_check)) 

Next, we print rows where the sum of disaggregated beneficiaries (total_ben_check) do not match the totals reported in the total_beneficiarios column so that we can return to the relevant parties for follow up.

Undisaggregated beneficiaries

organizacion_implementadora	actividad_desc	ubicacion	total_ben_check	total_beneficiarios	no_esp_ben
SOCIO 2	PROMOCION MENSAJES CLAVES PARA LA COMUNIDAD ESCOLAR	U.E.C. DIVINA PASTORA (CATEDRAL)	0	244	244
SOCIO 4	INICIATIVAS PARA REINSERCION EDUCATIVA DE NNA FUERA DE LA ESCUELA	U.E. SAN JOSE DE COTIZA	18	26	8
SOCIO 6	FORMACION DOCENTE Y OTRO PERSONAL EDUCATIVO	MINISTERIO DEL PODER POPULAR PARA LA EDUCACION	0	398	398
SOCIO 6	FORMACION DOCENTE Y OTRO PERSONAL EDUCATIVO	CLUSTER DE EDUCACION	0	146	146

 

5. Outputs

5a. Cleaned wide version

# timestamp for all the files to be written below
now <- Sys.time()

For submissions to OCHA, this one aligns with their format

# turn on eval if you want the file
filename_wide <- paste0(format(now, "%y%m%d_%H%M_"), "5W_wide.csv")
write_csv(act2, filename_wide)

5b. Unique beneficiaries

This section pivots the dataset longer into tidy data, with one observation per instance of age and sex; this is necessary for easy plotting and analysis. Additionally, rows with no beneficiaries are filtered out.

The dataset u_ben is created by taking the highest number of beneficiaries by age and sex group by location of all non-recurrent beneficiaries. The columns are then filtered down to create a more usable dataset. We will use this dataset for any operations related to unique beneficiaries (individuals), including comparisons with census data to determine reach and coverage as well as geographic analysis. We drop activity information from this dataset as it only contains the max of per disaggregation group per location anyway.

# pivoting longer and creating new dataframe
u_ben <- act2 %>% 
  select(-t_f_ben, -total_ben_check) %>% # remember to drop them
  relocate(mes_solo) %>% # moves mes_solo to the first variable
  pivot_longer(m_0_3:no_esp_ben, 
               names_to = "desagregacion", values_to = "beneficiarios") %>% 
  filter(recurrente_beneficiarios == FALSE) %>%
  filter(beneficiarios != 0) %>% # empty cells are 0 in the 5W table
  group_by(ubicacion, desagregacion) %>% 
  slice(which.max(beneficiarios)) %>% 
  ungroup() %>% 
  select(mes_solo, estado, pcode1, municipio, pcode2,parroquia, pcode3, 
         ubicacion, latitud, longitud,
         desagregacion, beneficiarios)

# writing csv of u_ben with datestamp
# turn on eval if you want the file 
filename_u_ben <- paste0(format(now, "%y%m%d_%H%M_"), "u_ben.csv")
write_csv(u_ben, filename_u_ben)

5c. Beneficiaries by activity

The dataset act_ben is for activity-wise analysis. Only the rows marked as recurrente_beneficiarios == FALSE have been selected. The first instance of recurring beneficiaires is marked FALSE, with all subsequent entries being marked TRUE. Similar to u_ben, it has also been pivoted longer into tidy data.

Please not that there is double counting in this dataset as specific beneficiaries might been reached by more than one activity. For geographic analysis, please use u_ben.

# This is the dataset for beneficiaries
act_ben <- act2 %>%
  select(-t_f_ben, -total_ben_check) %>% # remember to drop them
  relocate(mes_solo) %>% # moves mes_solo to the first variable
  filter(recurrente_beneficiarios == FALSE) %>%
  pivot_longer(m_0_3:no_esp_ben, 
               names_to = "desagregacion", values_to = "beneficiarios") %>% 
  filter(beneficiarios != 0) %>% # empty cells are 0 in the 5W table
  group_by(ubicacion, desagregacion) %>% 
  select(mes_solo, nombre_del_proyecto, organizacion_implementadora, 
         estado, pcode1, municipio, pcode2, parroquia, pcode3, 
         ubicacion, latitud, longitud,
         actividad_codigo, actividad_desc, act_covid,
         desagregacion, beneficiarios)

# writing csv act_ben with datestamp
# turn on eval if you want the file 
filename_act_ben <- paste0(format(now, "%y%m%d_%H%M_"), "act_ben.csv")
write_csv(act_ben, filename_act_ben)

5d. Comparing u_ben, act_ben and difference

Please note that this still has that radio messaging activity lumped in with the totals.

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##          [,1]
## [1,]  3660609
## [2,]  5673040
## [3,] -2012431

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##      [,1]        
## [1,] "3,660,609" 
## [2,] "5,673,040" 
## [3,] "-2,012,431"

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B. Reporting on the 5W data

 

This is an entirely automated report – all charts and tables, as well as all figures within the report have been generated from the data, with no manual input. This report makes use of the outputs of the 5W_cleaning section above. This set of 5W data pertains to the Education Cluster in Venezuela and has had partner information removed. Code will not be shown in this section – you can download the Rmd at the top-left corner of this doucument.

 

1. Summary of beneficiaries by activity, with sex ratio

actividad	total	percent_of_total	male	female	sex_ratio
DISTRIBUCION DE KITS DE MATERIALES ESCOLARES	471,568	50.91	232,197	239,372	0.97
ALIMENTACION ESCOLAR	156,472	16.89	72,035	84,437	0.85
EDUCACION A DISTANCIA	132,258	14.28	64,240	68,018	0.94
APOYO PSICOEDUCATIVO PARA NNA	120,887	13.05	54,859	66,028	0.83
FORMACION DOCENTE Y OTRO PERSONAL EDUCATIVO	17,238	1.86	3,470	13,224	0.26
ACTIVIDADES CON ADOLESCENTES Y JOVENES DE NIVELACION, HABILIDADES PARA LA VIDA Y CAPACITACION TECNICA	14,016	1.51	6,336	7,680	0.82
BECAS Y OTROS INCENTIVOS PARA DOCENTES Y PERSONAL	5,744	0.62	1,572	4,172	0.38
ACTIVIDADES RECREATIVAS	4,317	0.47	1,998	2,319	0.86
INICIATIVAS PARA REINSERCION EDUCATIVA DE NNA FUERA DE LA ESCUELA	3,869	0.42	2,114	1,755	1.2

A total of 728,408 individuals have been reached to date. In terms of frequencies (inclusive of double counting), 926,369 have been reached.

Additionally, the 4,746,671 beneficiary frequencies reached by the activity PROMOCION MENSAJES CLAVES PARA LA COMUNIDAD ESCOLAR have been removed from the totals in this report as the activity consists of solely radio messaging.

 

2. Beneficiaries by age group

figures are unique beneficiaries/individuals

 

2a. Beneficiaries by age compared to 2017 Enrollment

With reference to the 2017 Matricula dataset, we can see that the Education programme has reached an overall average of 9% of schoolgoing children aged 3-17 nationwide. Children aged 3-17 consitute 88% of all UNICEF beneficiaries.

Edad grupo	beneficiarios	matricula2017	percent_total
3-6	121,169	1,438,475	8.423
7-12	352,808	3,252,505	10.85
12-17	169,771	2,205,724	7.697

 

3. Changes since previous month

The number of individuals reached has increased by 175,005 in the past month, reaching a total of 728,408. The number of beneficiary frequencies reached has increased by 218,443 in the same period, reaching a total of 926,369.

 

3a. Progress by activity by month

mouse over to see details

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Progress in recent months has largely been due to the distribution of education kits and distance learning.

 

4. Summaries by geography

4a. Beneficiaries by state

 

4b. Number of schools by state

A total of 2,229 schools have been reached by UNICEF; 44% are from Miranda and Zulia alone.

 

4c. Scatterplot of municipalities

logarithmic scale; larger points indicate more beneficiaries reached, darker blues indicate more activity types

mouse over municipalities to see beneficiaries and distinct activities

A total of 110 municipalities were reached by the UNICEF Education programme.

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4d. Top 10 municipalities by reach and coverage

by beneficiaries

estado	municipio	beneficiarios
DISTRITO CAPITAL	LIBERTADOR	80482
MIRANDA	SUCRE	59176
ZULIA	MARACAIBO	55370
BOLIVAR	CARONI	37908
ZULIA	SAN FRANCISCO	29369
AMAZONAS	ATURES	22430
LARA	IRIBARREN	21977
BOLIVAR	HERES	18293
DELTA AMACURO	TUCUPITA	17953
BOLIVAR	CEDENO	15681

by coverage

estado	municipio	coverage_percent
TACHIRA	FERNANDEZ FEO	87
TACHIRA	AYACUCHO	79
ZULIA	MACHIQUES DE PERIJA	79
AMAZONAS	AUTONOMO AUTANA	76
TACHIRA	SAMUEL DARIO MALDONADO	75
TACHIRA	PANAMERICANO	72
MIRANDA	PLAZA	68
TACHIRA	INDEPENDENCIA	68
TACHIRA	JUNIN	65
MIRANDA	EL HATILLO	64

Together, the 10 municipalities with the highest reach (above left) form 49% of the 728,408 beneficiaries reached. The average coverage of the school-age population in the municipalities where UNICEF is present is 20%. Coverage refers to the percentage of enrolled children (aged 3-17 years) reached by UNICEF.

 

4e. Histogram of Coverage

Below is a histogram of munciipalities where UNICEF is present showing the coverage of enrolled children (aged 3-17). Of note, we have reached 10% or less of the population in 55 out of the 110 in which we operate. This is in addition to the 226 where no UNICEF Education activities have occurred.

 

5. Reports about partners

5a. Summary by partner

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5b. Number of activity types implemented by each partner

partner	SOCIO 2	SOCIO 1	SOCIO 4	SOCIO 5	UNICEF	SOCIO 3	SOCIO 6	SOCIO 7	SOCIO 10	SOCIO 8
act_types	9	8	8	6	6	4	2	2	1	1

 

5c. Partners’ progress over time

mouse over for details

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5d. Summary table of partners’ achievements

organizacion_implementadora	beneficiarios	percent_of_total	male	female	sex_ratio	municipalities
UNICEF	353,944	38.21	168,281	185,664	0.91	74
SOCIO 1	160,260	17.3	79,958	80,302	1	92
SOCIO 2	145,997	15.76	65,390	80,607	0.81	51
SOCIO 5	113,845	12.29	50,511	63,334	0.8	10
SOCIO 4	86,074	9.29	42,534	43,540	0.98	17
SOCIO 7	31,322	3.38	14,750	16,572	0.89	10
SOCIO 8	29,471	3.18	15,379	14,092	1.09	17
SOCIO 10	2,461	0.27	1,151	1,310	0.88	1
SOCIO 3	2,423	0.26	862	1,561	0.55	3
SOCIO 6	572	0.06	5	23	0.22	7

 

6. Maps and reference table

 

6a. Maps at municipal level

 

6b. Reference table – municipal level

use UNICEF_present to filter to municipalities where the Education programme operates

CA01.05 Promocion de mensajes claves para la comunidad escolar is not included

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